Update README.md

README.md

@@ -57,23 +57,23 @@ The model's output is evaluated using metrics such as ROUGE, which measures over
 The summarization model was trained on a dataset of press releases scraped from various party websites. These press releases were selected to represent diverse political perspectives and topics, ensuring that the model learned to generate summaries across a wide range of political content.
 Data Collection:
 
-    Source: Press releases from official party websites, which often contain detailed statements, policy announcements, and responses to current events. These documents were chosen because of their structured format and consistent language use.
+Source: Press releases from official party websites, which often contain detailed statements, policy announcements, and responses to current events. These documents were chosen because of their structured format and consistent language use.
 
-    Preprocessing: The scraped text was cleaned and preprocessed, removing extraneous HTML tags, irrelevant information, and ensuring that the text content was well-formatted for model training.
+Preprocessing: The scraped text was cleaned and preprocessed, removing extraneous HTML tags, irrelevant information, and ensuring that the text content was well-formatted for model training.
 
-    Text Format: The press releases were processed into suitable text pairs: the original full text as the input and a human-crafted summary (if available) or a custom summary generated by the developers as the target output.
+Text Format: The press releases were processed into suitable text pairs: the original full text as the input and a human-crafted summary (if available) or a custom summary generated by the developers as the target output.
 
 Training Objective:
 
-    The model was fine-tuned using these press releases to learn the task of abstractive summarization — generating concise, fluent summaries of longer political texts.
+The model was fine-tuned using these press releases to learn the task of abstractive summarization — generating concise, fluent summaries of longer political texts.
 
-    The model was trained to capture key information and context, while avoiding irrelevant details, ensuring that it could produce summaries that accurately reflect the essence of each release.
+The model was trained to capture key information and context, while avoiding irrelevant details, ensuring that it could produce summaries that accurately reflect the essence of each release.
 
 Training Strategy:
 
-    Supervised Learning: The model was trained using supervised learning, where each input (press release) was paired with a corresponding summary, enabling the model to learn the mapping from a long document to a short, concise summary.
+Supervised Learning: The model was trained using supervised learning, where each input (press release) was paired with a corresponding summary, enabling the model to learn the mapping from a long document to a short, concise summary.
 
-    Optimization: During training, the model's parameters were adjusted using gradient descent and the cross-entropy loss function, which penalizes incorrect predictions and encourages the generation of summaries that match the target.
+Optimization: During training, the model's parameters were adjusted using gradient descent and the cross-entropy loss function, which penalizes incorrect predictions and encourages the generation of summaries that match the target.
 
 This training process allowed the model to learn not only the specific language patterns commonly found in political press releases but also the broader context of political discourse.